{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# numpy 科学计算库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成随机数（多维数组）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.67230722,  0.92320551,  0.55412357,  0.67838681],\n",
       "       [ 0.15995348,  0.82559849,  0.81119944,  0.81135556],\n",
       "       [ 0.53625934,  0.53767243,  0.74293748,  0.19497616],\n",
       "       [ 0.45619009,  0.86728338,  0.59418446,  0.94552662]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran = np.random.rand(4,4)\n",
    "ran"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数组运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.88659088e-02,   4.49763178e-01,   2.72939384e-03,\n",
       "          2.06430545e-02],\n",
       "       [  1.09631922e-08,   1.47125827e-01,   1.23388991e-01,\n",
       "          1.23626676e-01],\n",
       "       [  1.96674798e-03,   2.01919219e-03,   5.12298030e-02,\n",
       "          7.93990314e-08],\n",
       "       [  3.90353857e-04,   2.40774367e-01,   5.48545806e-03,\n",
       "          5.71133483e-01]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ran**10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.48741524,  1.08318244,  1.80465163,  1.47408526],\n",
       "       [ 6.25181758,  1.21124252,  1.23274247,  1.23250526],\n",
       "       [ 1.86476938,  1.85986848,  1.34600828,  5.12883207],\n",
       "       [ 2.19206866,  1.15302567,  1.68297905,  1.05761168]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/ran"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 内置统计学函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02404611,  0.00856863, -0.00717491,  0.01792569],\n",
       "       [ 0.00856863,  0.10766162, -0.00707782,  0.05709484],\n",
       "       [-0.00717491, -0.00707782,  0.05158568, -0.03444119],\n",
       "       [ 0.01792569,  0.05709484, -0.03444119,  0.05263639]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cov(ran)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.16840644, -0.20371803,  0.50386061],\n",
       "       [ 0.16840644,  1.        , -0.09497394,  0.75844372],\n",
       "       [-0.20371803, -0.09497394,  1.        , -0.66095285],\n",
       "       [ 0.50386061,  0.75844372, -0.66095285,  1.        ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(ran)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.67230722,  0.15995348,  0.53625934,  0.45619009])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#取ran的第一列数据\n",
    "a = ran[:,0]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.672307</td>\n",
       "      <td>0.923206</td>\n",
       "      <td>0.554124</td>\n",
       "      <td>0.678387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.159953</td>\n",
       "      <td>0.825598</td>\n",
       "      <td>0.811199</td>\n",
       "      <td>0.811356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.536259</td>\n",
       "      <td>0.537672</td>\n",
       "      <td>0.742937</td>\n",
       "      <td>0.194976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.456190</td>\n",
       "      <td>0.867283</td>\n",
       "      <td>0.594184</td>\n",
       "      <td>0.945527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.672307  0.923206  0.554124  0.678387\n",
       "1  0.159953  0.825598  0.811199  0.811356\n",
       "2  0.536259  0.537672  0.742937  0.194976\n",
       "3  0.456190  0.867283  0.594184  0.945527"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(ran,index = [i for i in range(4)],columns = [i for i in range(4)])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81119943811161055"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,2] #取第2行第3列数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 函数应用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.451997</td>\n",
       "      <td>0.852308</td>\n",
       "      <td>0.307053</td>\n",
       "      <td>0.460209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.025585</td>\n",
       "      <td>0.681613</td>\n",
       "      <td>0.658045</td>\n",
       "      <td>0.658298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.287574</td>\n",
       "      <td>0.289092</td>\n",
       "      <td>0.551956</td>\n",
       "      <td>0.038016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.208109</td>\n",
       "      <td>0.752180</td>\n",
       "      <td>0.353055</td>\n",
       "      <td>0.894021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.451997  0.852308  0.307053  0.460209\n",
       "1  0.025585  0.681613  0.658045  0.658298\n",
       "2  0.287574  0.289092  0.551956  0.038016\n",
       "3  0.208109  0.752180  0.353055  0.894021"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.apply(lambda x :x**2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 描述性统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.243316</td>\n",
       "      <td>0.643798</td>\n",
       "      <td>0.467527</td>\n",
       "      <td>0.512636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.177156</td>\n",
       "      <td>0.246624</td>\n",
       "      <td>0.165611</td>\n",
       "      <td>0.362714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.025585</td>\n",
       "      <td>0.289092</td>\n",
       "      <td>0.307053</td>\n",
       "      <td>0.038016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.162478</td>\n",
       "      <td>0.583483</td>\n",
       "      <td>0.341555</td>\n",
       "      <td>0.354660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.247842</td>\n",
       "      <td>0.716897</td>\n",
       "      <td>0.452506</td>\n",
       "      <td>0.559253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.328680</td>\n",
       "      <td>0.777212</td>\n",
       "      <td>0.578478</td>\n",
       "      <td>0.717229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.451997</td>\n",
       "      <td>0.852308</td>\n",
       "      <td>0.658045</td>\n",
       "      <td>0.894021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3\n",
       "count  4.000000  4.000000  4.000000  4.000000\n",
       "mean   0.243316  0.643798  0.467527  0.512636\n",
       "std    0.177156  0.246624  0.165611  0.362714\n",
       "min    0.025585  0.289092  0.307053  0.038016\n",
       "25%    0.162478  0.583483  0.341555  0.354660\n",
       "50%    0.247842  0.716897  0.452506  0.559253\n",
       "75%    0.328680  0.777212  0.578478  0.717229\n",
       "max    0.451997  0.852308  0.658045  0.894021"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制随机漫步"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20b3a2cb0b8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h3Llzkv9HbVSpUoUAqI2eAZCbmxsREX377bc8Eufu3bu8TvbsgczE7Q4ODvyz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\nq1fnE0K3bt0q0GuzP0YvLy8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2W3Tt2jVHRp+tbj106BB69OiBI0eOYNCgQfmis86SWzDV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\nQRDKBBH4giAIZcL/B45gIvvA+iZnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20b3a458438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.plot(np.random.randn(1000).cumsum(),'k',label = 'one')\n",
    "ax.plot(np.random.randn(1000).cumsum(),'k--',label = 'two')\n",
    "ax.plot(np.random.randn(1000).cumsum(),'k.',label = 'three')\n",
    "ax.legend()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
